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Image Features: A General Process

Step 1 - Feature Detection: identify distinctive points in our images. We call these points features.
Step 2 - Feature Description: associate a descriptor for each feature from its neighborhood.
Step 3 - Feature Matching: we use these descriptors to match features across two or more images.


Feature DetectionFeature Define
Features: Points of interest in an image defined by its image pixel coordinates [u, v].


Points of interest should have the following ch ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2022/10/03/2022q3/164-3-camera-and-images/" title="MOB LEC3 Cameras and Images">     <img class="post_bg" data-lazy-src="https://image.discover304.top/blog-img/s21513409222022.png?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="MOB LEC3 Cameras and Images"></a></div><div class="recent-post-info"><a class="article-title" href="/2022/10/03/2022q3/164-3-camera-and-images/" title="MOB LEC3 Cameras and Images">MOB LEC3 Cameras and Images</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2022-10-03T02:09:06.000Z" title="Created 2022-10-03 10:09:06">2022-10-03</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%9F%A5%E8%AF%86/">知识</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%A7%AF%E7%B4%AF/">积累</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a></span></div><div class="content">Introduction of computer visionComputer vision is a field of artificial intelligence (AI) that enables computers derive meaningful information from digital images, videos and other visual inputs obtained by a camera. 

Bucket of photons

Photons converted to electrons
Shift electrons along row for readout
The readout on the device will translate the analog signal to either grayscale images or RGB images

Image Formation

pinhole camera, optical centre, focal length
Stereo Cameras



Image filter ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2022/10/01/2022q3/166-3-digital-speech-signals/" title="SP Module 3 – Digital Speech Signals">     <img class="post_bg" data-lazy-src="https://image.discover304.top/blog-img/s11220010012022.png?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="SP Module 3 – Digital Speech Signals"></a></div><div class="recent-post-info"><a class="article-title" href="/2022/10/01/2022q3/166-3-digital-speech-signals/" title="SP Module 3 – Digital Speech Signals">SP Module 3 – Digital Speech Signals</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2022-10-01T09:46:46.000Z" title="Created 2022-10-01 17:46:46">2022-10-01</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%9F%A5%E8%AF%86/">知识</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%A7%AF%E7%B4%AF/">积累</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a></span></div><div class="content">Time domainSound is a wave of pressure travelling through a medium, such as air. We can plot the variation in pressure (captured by microphone) against time to visualise the waveform.
Sound sourceAir flow from the lungs is the power source for generating a basic source of sound either using the vocal folds or at a constriction made anywhere in the vocal tract.


somehthing about pressure with our vocal folds, the air flow is slow, its only the power source of sound, the pressure change is the ke ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2022/10/01/2022q3/166-2-acoustics-of-consonants-and-vowels/" title="SP Module 2 – Acoustics of Consonants and Vowels">     <img class="post_bg" data-lazy-src="https://image.discover304.top/blog-img/s11220010012022.png?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="SP Module 2 – Acoustics of Consonants and Vowels"></a></div><div class="recent-post-info"><a class="article-title" href="/2022/10/01/2022q3/166-2-acoustics-of-consonants-and-vowels/" title="SP Module 2 – Acoustics of Consonants and Vowels">SP Module 2 – Acoustics of Consonants and Vowels</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2022-10-01T09:41:31.000Z" title="Created 2022-10-01 17:41:31">2022-10-01</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%9F%A5%E8%AF%86/">知识</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%A7%AF%E7%B4%AF/">积累</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a></span></div><div class="content">
WaveformThe waveform and a definition of the fundamental period.

Fundamental period is the lowest frequency of a vibration object.
Types of waveformSimple, complex, periodic, aperiodic, transient, and continuous waveforms.



SpectrumThe spectrum, its spectral envelope, and harmonics (Frequency components of a complex periodic sound, peaks in spectrum, $H_1$ has the same frequency value to $F_0$, every $H$ is multiple of $H_1$), and formant.

SpectrogramA 3-dimensional figure plotting amount o ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2022/10/01/2022q3/166-1-phonetics-and-representations-of-speech/" title="SP Module 1 - Phonetics and Representations of Speech">     <img class="post_bg" data-lazy-src="https://image.discover304.top/blog-img/s11220010012022.png?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="SP Module 1 - Phonetics and Representations of Speech"></a></div><div class="recent-post-info"><a class="article-title" href="/2022/10/01/2022q3/166-1-phonetics-and-representations-of-speech/" title="SP Module 1 - Phonetics and Representations of Speech">SP Module 1 - Phonetics and Representations of Speech</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2022-10-01T09:24:35.000Z" title="Created 2022-10-01 17:24:35">2022-10-01</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%9F%A5%E8%AF%86/">知识</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%A7%AF%E7%B4%AF/">积累</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a></span></div><div class="content">Introduction to the International Phonetic AlphabetA set of symbols with which any language can be transcribed. Interactive IPA Chart.

Vocal anatomyWe use a lot more than just our mouth to produce speech

ConsonantsVoice, place, manner

which (voice or voiceless) -&gt; where (at voice tract) -&gt; how strong (constriction level)


The first consonant chart contains symbols for consonants produced with the pulmonic airstream mechanism.






Non-pulmonic consonants includes symbols representing  ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2022/10/01/2022q3/166-0-getting-started/" title="SP Module 0 – Getting Started">     <img class="post_bg" data-lazy-src="https://image.discover304.top/blog-img/s11220010012022.png?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="SP Module 0 – Getting Started"></a></div><div class="recent-post-info"><a class="article-title" href="/2022/10/01/2022q3/166-0-getting-started/" title="SP Module 0 – Getting Started">SP Module 0 – Getting Started</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2022-10-01T03:20:19.000Z" title="Created 2022-10-01 11:20:19">2022-10-01</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%9F%A5%E8%AF%86/">知识</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%A7%AF%E7%B4%AF/">积累</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a></span></div><div class="content">
Test to Speech Synthesis TTSthe generation of speech from text input


Automatic Speech Recognition ASRthe transcription of speech into text


Key ideas

PHON – phonetics and phonology
SIGNALS – signal processing, with a focus on speech signals
TTS – text-to-speech synthesis
ASR – automatic speech recognition
SKILLS – maths, computing, writing




The phonetics modules in this course are intended to complement the speech processing content.


10 modules, Wednesday weekly lab, video before lectu ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2022/09/26/2022q3/164-2-hardware-and-software-atchitectures/" title="MOB LEC2 Hardware and Software Architectures">     <img class="post_bg" data-lazy-src="https://image.discover304.top/blog-img/s21513409222022.png?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="MOB LEC2 Hardware and Software Architectures"></a></div><div class="recent-post-info"><a class="article-title" href="/2022/09/26/2022q3/164-2-hardware-and-software-atchitectures/" title="MOB LEC2 Hardware and Software Architectures">MOB LEC2 Hardware and Software Architectures</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2022-09-26T13:31:00.000Z" title="Created 2022-09-26 21:31:00">2022-09-26</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%9F%A5%E8%AF%86/">知识</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%A7%AF%E7%B4%AF/">积累</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a></span></div><div class="content">Sensors for robot perception
Sensors: Sensor is a device that measures or detects a property of the environment, or changes to a property.


Categorization of sensors: Exteroceptive (extero or surroundings), Proprioceptive (proprio or internal).





Type of Sensor
Feature
Weakness
Future Trend
More words




Essential for robot to perceive environment with its rich semantics.
?
HD, wide dynamic ranges
Comparison Metrics: Resolution, Field of view, Dynamics range



This simulates human binocula ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2022/09/19/2022q3/164-1-introduction/" title="MOB LEC1 Introduction">     <img class="post_bg" data-lazy-src="https://image.discover304.top/blog-img/s21513409222022.png?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="MOB LEC1 Introduction"></a></div><div class="recent-post-info"><a class="article-title" href="/2022/09/19/2022q3/164-1-introduction/" title="MOB LEC1 Introduction">MOB LEC1 Introduction</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2022-09-19T00:55:53.000Z" title="Created 2022-09-19 08:55:53">2022-09-19</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%9F%A5%E8%AF%86/">知识</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%A7%AF%E7%B4%AF/">积累</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a></span></div><div class="content">Meaning of robot
Origin of the Term: The word “robot” was introduced to the public by Czech (捷克共和国) writer Karel Čapek (卡雷尔·恰佩克) in his science-fiction play R.U.R. (Rossum’s Universal Robots) in 1920. In Czech language, “robota” means “labour” or “work”.


Original purpose of robots: automatic&#x2F;autonomous labour that frees humans from tedious jobs

Use cases of robotPeople fear

Dangerous: exploration, chemical spill cleanup, disarming bombs, disaster cleanup.

People tired

Boring or repeti ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2022/03/23/2022q1/157-mmdetection-running-mannual/" title="【AI框架】Mmdetection3dlab 开发日志">     <img class="post_bg" data-lazy-src="https://image.discover304.top/blog-img/s17293303202022-2022320172935.png?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="【AI框架】Mmdetection3dlab 开发日志"></a></div><div class="recent-post-info"><a class="article-title" href="/2022/03/23/2022q1/157-mmdetection-running-mannual/" title="【AI框架】Mmdetection3dlab 开发日志">【AI框架】Mmdetection3dlab 开发日志</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2022-03-23T05:09:32.000Z" title="Created 2022-03-23 13:09:32">2022-03-23</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E6%8A%80%E6%9C%AF/">技术</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD/">人工智能</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Docker/">Docker</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%95%99%E7%A8%8B/">教程</a></span></div><div class="content">待办清单：
网络整体：x_net.py
特征融合网络层：x_net_fusion_layers.py
网络配置文件：xnet

开发日志：
日志01使用pycharm的SSH连接docker，不如设置pycharm的编译器为docker中的python。这样做的优势有三：

不需要通过ssh传输图像，pycharm的运行速度更快。
因为docker同步了工作目录，不需要使用pycharm来同步，节省时间。
不需要重新弄配置pycharm。

需要修改的有两个位置，一个是configs中的配置文件，一个是mmdet3d中的models相关文件。参照官方教程：教程 1: 学习配置文件和教程 4: 自定义模型
整个框架的思路是从配置文件中去找对应的模型，程序会在一开始把模型全部注册到一个位置，然后使用配置文件中的type关键字去搜索，然后使用其他的作为参数输入，具体需要什么参数由模型决定。
阅读代码的时候发现，框架对自编码器有一定的支持，这一点在完成主干网络的构建后深入调查一下。

日志02
注意到代码中 fusion layer 本来是分出来的，但是因为代码复用的问题，实际上并没有分出来， ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2021/12/02/2021q4/107-4-dl-pdpd-base/" title="【深度学习】框架：PaddlePaddle基础">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/dl/02/dog-end-world.jpg?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="【深度学习】框架：PaddlePaddle基础"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/12/02/2021q4/107-4-dl-pdpd-base/" title="【深度学习】框架：PaddlePaddle基础">【深度学习】框架：PaddlePaddle基础</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-12-02T07:53:26.000Z" title="Created 2021-12-02 15:53:26">2021-12-02</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a></span></div><div class="content">说明
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PaddlePaddle概述





 







PaddlePaddle概述
PaddlePaddle概述
PaddlePaddle简介
为什么要学PaddlePaddle
什么是PaddlePaddle
PaddlePaddle优点
PaddlePaddle缺点
国际竞赛获奖情况
行业应用
课程概览
学习资源


































 







知识讲解
什么是PaddlePaddle
Ø
PaddlePaddle（Parallel&#160;Distributed&#160;Deep&#160;Learning，中文名飞桨）是百度公司推出的开源、易学习、易使用的分布式深度学习平台
Ø
源于产业实践，在实际中有着优异表现
Ø
支持多种机器学习经典模型

















 







知识讲解
为什么学习Pad ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2021/12/02/2021q4/107-3-dl-tf/" title="【深度学习】框架：TensorFlow1">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/dl/02/dog-end-world.jpg?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="【深度学习】框架：TensorFlow1"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/12/02/2021q4/107-3-dl-tf/" title="【深度学习】框架：TensorFlow1">【深度学习】框架：TensorFlow1</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-12-02T07:53:25.000Z" title="Created 2021-12-02 15:53:25">2021-12-02</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a></span></div><div class="content">说明
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TensorFlow概述





 







Tensorflow概述
Tensorflow概述
Tensorflow简介
什么是Tensorflow
Tensorflow的特点
Tensorflow的发展历史
Tensorflow体系结构
体系结构概述
单机模式与分布式
后端逻辑层次
基本概念
张量
数据流
操作
图和会话
变量和占位符
Tensorflow安装
案例1：快速开始
案例2：张量相加








TensorFlow简介





 







知识讲解
什么是Tensorflow
•&#160;TensorFlow由谷歌人工智能团队谷歌大脑（Google&#160;Brain）开发和维护的
开源深度学习平台，是目前人工智能领域主流的开发平台，在全世界有着广泛的用户群体。



















 







知识讲解
Tenso ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2021/12/02/2021q4/107-2-dl/" title="【深度学习】图像操作：OpenCV">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/dl/02/dog-end-world.jpg?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="【深度学习】图像操作：OpenCV"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/12/02/2021q4/107-2-dl/" title="【深度学习】图像操作：OpenCV">【深度学习】图像操作：OpenCV</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-12-02T07:53:24.000Z" title="Created 2021-12-02 15:53:24">2021-12-02</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a></span></div><div class="content">注意本教程OpenCV版本过旧。
说明
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计算机视觉基础





 







计算机视觉基础
计算机视觉基础
计算机视觉概述
计算机视觉的应用
什么是计算机视觉
计算机视觉相关学科
人眼成像原理
计算机成像原理
数字图像处理基础
灰度级与灰度图像
图像采样与分辨率
彩色图像与色彩空间
颜色空间变化
常用图像处理技术
计算机视觉的应用
什么是计算机视觉
计算机视觉与人工智能









计算机视觉概览





 







知识讲解
什么是计算机视觉
•&#160;计算机视觉在广义上是和图像相关的技术总称。包括图像的采集获取，图
像的压缩编码，图像的存储和传输，图像的合成，三维图像重建，图像增强，图像修复，图像的分类和识别，目标的检测、跟踪、表达和描述，特征提取，图像的显示和输出等等。
•&#160;随着计算机视觉在各种场景的应用和发展，已有的图像技术也在不断的更
新和扩展。



 ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2021/12/01/2021q4/108-3-dl-ex/" title="【深度学习】实例第四部分：PaddlePaddle">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/dl/space_work.jpg?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="【深度学习】实例第四部分：PaddlePaddle"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/12/01/2021q4/108-3-dl-ex/" title="【深度学习】实例第四部分：PaddlePaddle">【深度学习】实例第四部分：PaddlePaddle</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-12-01T02:16:47.000Z" title="Created 2021-12-01 10:16:47">2021-12-01</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a></span></div><div class="content">注意：全部代码为PaddlePaddle1版本的代码
Helloworld1234567891011121314# helloworld示例import paddle.fluid as fluid# 创建两个类型为int64, 形状为1*1张量x = fluid.layers.fill_constant(shape=[1], dtype=&quot;int64&quot;, value=5)y = fluid.layers.fill_constant(shape=[1], dtype=&quot;int64&quot;, value=1)z = x + y # z只是一个对象,没有run,所以没有值# 创建执行器place = fluid.CPUPlace() # 指定在CPU上执行exe = fluid.Executor(place) # 创建执行器result = exe.run(fluid.default_main_program(),                 fetch_list=[z]) #返回哪个结果print(result) # result为多维张量

张量操作 ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2021/12/01/2021q4/108-2-dl-ex/" title="【深度学习】实例第三部分：TensorFlow">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/dl/space_work.jpg?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="【深度学习】实例第三部分：TensorFlow"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/12/01/2021q4/108-2-dl-ex/" title="【深度学习】实例第三部分：TensorFlow">【深度学习】实例第三部分：TensorFlow</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-12-01T02:16:42.000Z" title="Created 2021-12-01 10:16:42">2021-12-01</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a></span></div><div class="content">注意：此代码全部为TensorFlow1版本。
查看Tensorflow版本1234567891011from __future__ import absolute_import, division, print_function, unicode_literals# 导入TensorFlow和tf.kerasimport tensorflow as tffrom tensorflow import keras# 导入辅助库import numpy as npimport matplotlib.pyplot as pltprint(tf.__version__)

Helloworld程序1234567# tf的helloworld程序import tensorflow as tfhello = tf.constant(&#x27;Hello, world!&#x27;)  # 定义一个常量sess = tf.Session()  # 创建一个sessionprint(sess.run(hello))  # 计算sess.close()

张量相加1234567891011121314 ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2021/12/01/2021q4/108-1-dl-ex/" title="【深度学习】实例第二部分：OpenCV">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/dl/space_work.jpg?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="【深度学习】实例第二部分：OpenCV"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/12/01/2021q4/108-1-dl-ex/" title="【深度学习】实例第二部分：OpenCV">【深度学习】实例第二部分：OpenCV</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-12-01T02:16:31.000Z" title="Created 2021-12-01 10:16:31">2021-12-01</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a></span></div><div class="content">OpenCV安装执行以下命令安装opencv-python库（核心库）和opencv-contrib-python库（贡献库）。注意：命令拷贝后要合成一行执行，中间不要换行。
12345# 安装opencv核心库pip3 install  --user opencv-python==3.4.2.16 --index-url https://pypi.tuna.tsinghua.edu.cn/simple/  --trusted-host https://pypi.tuna.tsinghua.edu.cn# 安装opencv贡献库pip3 install  --user opencv-contrib-python==3.4.2.16 --index-url https://pypi.tuna.tsinghua.edu.cn/simple/  --trusted-host https://pypi.tuna.tsinghua.edu.cn



OpenCV基本操作读取、图像、保存图像12345678910111213# 读取图像import cv2im = cv2.imread(&quo ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2021/12/01/2021q4/108-0-dl-ex/" title="【深度学习】实例第一部分：基础理论">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/dl/space_work.jpg?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="【深度学习】实例第一部分：基础理论"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/12/01/2021q4/108-0-dl-ex/" title="【深度学习】实例第一部分：基础理论">【深度学习】实例第一部分：基础理论</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-12-01T01:30:44.000Z" title="Created 2021-12-01 09:30:44">2021-12-01</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a></span></div><div class="content">自定义感知机123456789101112131415161718192021222324252627282930313233343536373839# 00_percetron.py# 实现感知机# 实现逻辑和def AND(x1, x2):    w1, w2, theta = 0.5, 0.5, 0.7    tmp = x1 * w1 + x2 * w2    if tmp &lt;= theta:        return 0    else:        return 1print(AND(1, 1))print(AND(1, 0))# 实现逻辑或def OR(x1, x2):    w1, w2, theta = 0.5, 0.5, 0.2    tmp = x1 * w1 + x2 * w2    if tmp &lt;= theta:        return 0    else:        return 1print(OR(0, 1))print(OR(0, 0))# 实现异或def XOR(x1, x2):    s1 = not AND(x1, x2) ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2021/11/30/2021q4/107-1-dl-cnn/" title="【深度学习】基础 肆：卷积神经网络">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/dl/machine-girl.jpg?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="【深度学习】基础 肆：卷积神经网络"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/11/30/2021q4/107-1-dl-cnn/" title="【深度学习】基础 肆：卷积神经网络">【深度学习】基础 肆：卷积神经网络</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-11-30T11:43:07.000Z" title="Created 2021-11-30 19:43:07">2021-11-30</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a></span></div><div class="content">注：封面画师：新雨林-触站
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卷积神经网络
卷积神经网络
卷积函数
离散卷积与多维卷积
什么是卷积神经网络
什么是卷积
卷积神经网络
卷积神经网络的用途
卷积运算
卷积运算的效果
案例2：图像卷积运算
卷积运算神经网络结构
典型CNN介绍
生活中的卷积
全连接神经网络的局限





卷积函数





 







知识讲解
什么是卷积
•&#160;“卷积”其实是一个数学概念，它描述一个函数和另一个函数在某个维度上
的加权“叠加”作用。函数定义如下：
其中，函数&#160;f&#160;和函数&#160;g&#160;是卷积对象，a&#160;为积分变量，星号“*”表示卷积。公式所示的操作，被称为连续域上的卷积操作。这种操作通常也被简记为如下公式：












 







知识讲解
离散卷积与多维卷积
•&#160;一般情况下，我们并不需 ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2021/11/30/2021q4/107-1-dl-back/" title="【深度学习】基础 叁：反向传播算法">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/dl/machine-girl.jpg?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="【深度学习】基础 叁：反向传播算法"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/11/30/2021q4/107-1-dl-back/" title="【深度学习】基础 叁：反向传播算法">【深度学习】基础 叁：反向传播算法</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-11-30T11:43:06.000Z" title="Created 2021-11-30 19:43:06">2021-11-30</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a></span></div><div class="content">注：封面画师：新雨林-触站
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反向传播
这里对反向传播的讲解比较奇怪，可能比较适合初学者理解。想要通过严谨的数学推导理解反向传播的同学，可以搜索一下。







 







反向传播算法
反向传播算法
什么是正向传播网络
什么是反向传播
反向传播算法
为什么需要反向传播
图解反向传播
反向传播计算
链式求导法则
案例1：通过反向传播计算偏导数












 







知识讲解
什么是正向传播网络
•&#160;前一层的输出作为后一层的输入的逻辑结构，每一层神经元仅与下一层的
神经元全连接，通过增加神经网络的层数虽然可为其提供更大的灵活性，让网络具有更强的表征能力，也就是说，能解决的问题更多，但随之而来的
数量庞大的网络参数的训练，一直是制约多层神经网络发展的一个重要
瓶颈。












 







知识讲解
什么是反向传播
•&#160;反向传播（B ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2021/11/30/2021q4/107-1-dl-loss-gd/" title="【深度学习】基础 贰：损失函数与梯度下降">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/dl/machine-girl.jpg?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="【深度学习】基础 贰：损失函数与梯度下降"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/11/30/2021q4/107-1-dl-loss-gd/" title="【深度学习】基础 贰：损失函数与梯度下降">【深度学习】基础 贰：损失函数与梯度下降</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-11-30T11:43:05.000Z" title="Created 2021-11-30 19:43:05">2021-11-30</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a></span></div><div class="content">注：封面画师：新雨林-触站
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损失函数与梯度下降
损失函数与梯度下降
损失函数
损失函数的作用
什么是损失函数
梯度下降
常用的损失函数
什么是梯度
梯度下降
导数与偏导数
学习率
梯度递减训练法则
梯度下降算法






损失函数





 







知识讲解
什么是损失函数
•&#160;损失函数（Loss&#160;Function），也有称之为代价函数（Cost&#160;Function），
用
来度量预测值和实际值之间的差异。












 







知识讲解
损失函数的作用
•&#160;度量决策函数f（x）和实际值之间的差异。•&#160;作为模型性能参考。损失函数值越小，说明预测输出和实际结果（也称期望
输出）之间的差值就越小，也就说明我们构建的模型越好。
学习的过程，就
是不断通过训练数据进行预测，不断调整预测输出与实 ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2021/11/30/2021q4/107-1-dl-perceptron/" title="【深度学习】基础 壹：感知机与神经网络">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/dl/machine-girl.jpg?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="【深度学习】基础 壹：感知机与神经网络"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/11/30/2021q4/107-1-dl-perceptron/" title="【深度学习】基础 壹：感知机与神经网络">【深度学习】基础 壹：感知机与神经网络</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-11-30T11:43:04.000Z" title="Created 2021-11-30 19:43:04">2021-11-30</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a></span></div><div class="content">注：封面画师：新雨林-触站
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感知机与神经网络
感知机与神经网络
感知机
感知机的功能
什么是感知机
如何实现感知机
激活函数
感知机的缺陷
多层感知机
神经网络
什么是神经网络
神经网络的功能
通用近似定理
什么是激活函数
为什么使用激活函数
深层网络的优点
常见激活函数






感知机





 







知识讲解
生物神经元
细胞体
（处理信息）
树突（收集信息）
轴突（传递信息）
突触
（输出信息）












 







知识讲解
生物神经网络












 







知识讲解
什么是感知机
•&#160;感知机（Perceptron），又称神经元（Neuron，对生物神经元进行了模仿）是神
经网络（深度学习）的起源算法，1958年由康奈尔大学心理学教授弗兰克·罗森布拉特（Frank&#160;Rosenb ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2021/11/30/2021q4/107-0-dl/" title="【深度学习】概述">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/dl/machine-girl.jpg?imageView2/2/h/300" onerror="this.onerror=null;this.src='/img/404.png'" alt="【深度学习】概述"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/11/30/2021q4/107-0-dl/" title="【深度学习】概述">【深度学习】概述</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-11-30T11:43:03.000Z" title="Created 2021-11-30 19:43:03">2021-11-30</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a></span></div><div class="content">注：封面画师：新雨林-触站
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深度学习概述
深度学习概述
引入
深度学习巨大影响
人工智能划时代事件
什么是深度学习
深度神经网络
深度学习与机器学习的关系
深度学习的定义
深度学习的特点
深度学习的应用
深度学习的优点
深度学习的缺点
深度学习与机器学习对比
课程内容与特点
为什么要学习深度学习
深度学习发展史
深度学习的特点
深度学习的应用
课程内容
课程特点
学习资源推荐
深度学习发展简史
深度网络演化过程






引入





 







知识讲解
人工智能划时代事件
•&#160;2016年3月，Google公司研发的AlphaGo以
4:1击败世界围棋顶级选手李世石。次年，AlphaGo2.0对战世界最年轻的围棋四冠王柯洁，以3:0击败对方。背后支撑AlphaGo具备如此强大能力的，就是“深度学习”（Deep&#160;Learning）。
 ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2021/11/30/2021q4/103-6-ml-opti/" title="【机器学习】第七部分：模型优化">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/AI-cover-black-white.webp" onerror="this.onerror=null;this.src='/img/404.png'" alt="【机器学习】第七部分：模型优化"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/11/30/2021q4/103-6-ml-opti/" title="【机器学习】第七部分：模型优化">【机器学习】第七部分：模型优化</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-11-30T06:19:00.000Z" title="Created 2021-11-30 14:19:00">2021-11-30</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/">机器学习</a></span></div><div class="content">验证曲线与学习曲线① 验证曲线验证曲线是指根据不同的评估系数，来评估模型的优劣. 例如，构建随机森林，树的数量不同，模型预测准确度有何不同？以下是一个验证曲线的示例：
1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556# 验证曲线示例import numpy as npimport sklearn.preprocessing as spimport sklearn.ensemble as seimport sklearn.model_selection as msimport matplotlib.pyplot as mpdata = []with open(&quot;../data/car.txt&quot;, &quot;r&quot;) as f:    for line in f.readlines():        data.append(line.replace(&quot;\n&quot;, &quot;&quot ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2021/11/25/2021q4/103-5-ml-metr/" title="【机器学习】第六部分：模型评估">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/AI-cover-black-white.webp" onerror="this.onerror=null;this.src='/img/404.png'" alt="【机器学习】第六部分：模型评估"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/11/25/2021q4/103-5-ml-metr/" title="【机器学习】第六部分：模型评估">【机器学习】第六部分：模型评估</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-11-25T06:31:14.000Z" title="Created 2021-11-25 14:31:14">2021-11-25</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/">机器学习</a></span></div><div class="content">性能度量① 错误率与精度错误率和精度是分类问题中常用的性能度量指标，既适用于二分类任务，也适用于多分类任务. 

错误率（error rate）：指分类错误的样本占样本总数的比例，即 （ 分类错误的数量 &#x2F; 样本总数数量）

精度（accuracy）：指分类正确的样本占样本总数的比例，即 （分类正确的数量 &#x2F; 样本总数数量）
$$精度 &#x3D; 1 - 错误率$$


② 查准率、召回率与F1得分错误率和精度虽然常用，但并不能满足所有的任务需求。例如，在一次疾病检测中，我们更关注以下两个问题：

检测出感染的个体中有多少是真正病毒携带者？
所有真正病毒携带者中，有多大比例被检测了出来？

类似的问题在很多分类场景下都会出现，“查准率”（precision）与“召回率”（recall）是更为适合的度量标准。对于二分类问题，可以将真实类别、预测类别组合为“真正例”（true positive）、“假正例”（false positive）、“真反例”（true negative）、“假反例”（false negative）四种情形，见下表：


样例总数：TP + F ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2021/11/25/2021q4/103-4-ml-pca/" title="【机器学习】第五部分：降维问题">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/AI-cover-black-white.webp" onerror="this.onerror=null;this.src='/img/404.png'" alt="【机器学习】第五部分：降维问题"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/11/25/2021q4/103-4-ml-pca/" title="【机器学习】第五部分：降维问题">【机器学习】第五部分：降维问题</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-11-25T06:31:07.000Z" title="Created 2021-11-25 14:31:07">2021-11-25</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/">机器学习</a></span></div><div class="content">参见：机器学习四大降维方法
PCA降维参见：【机器学习】降维——PCA（非常详细）参见：机器学习实战8-sklearn降维(PCA&#x2F;LLE)
LDA降维参见：【机器学习实战】降维方法的sklearn实现—-PCA和LDA
LLE降维参见：机器学习实战8-sklearn降维(PCA&#x2F;LLE)
拉普拉斯特征映射参见：python实现拉普拉斯特征图降维示例
</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2021/11/25/2021q4/103-3-ml-clus/" title="【机器学习】第四部分：聚类问题">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/AI-cover-black-white.webp" onerror="this.onerror=null;this.src='/img/404.png'" alt="【机器学习】第四部分：聚类问题"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/11/25/2021q4/103-3-ml-clus/" title="【机器学习】第四部分：聚类问题">【机器学习】第四部分：聚类问题</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-11-25T06:31:06.000Z" title="Created 2021-11-25 14:31:06">2021-11-25</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/">机器学习</a></span></div><div class="content">聚类问题概述聚类（cluster）与分类（class）问题不同，聚类是属于无监督学习模型，而分类属于有监督学习。聚类使用一些算法把样本分为N个群落，群落内部相似度较高，群落之间相似度较低。在机器学习中，通常采用“距离”来度量样本间的相似度，距离越小，相似度越高；距离越大，相似度越低.
相似度度量方式① 欧氏距离相似度使用欧氏距离来进行度量. 坐标轴上两点$x_1, x_2$之间的欧式距离可以表示为：$$|x_1-x_2| &#x3D; \sqrt{(x_1-x_2)^2}$$平面坐标中两点$(x_1, y_1), (x_2, y_2)$欧式距离可表示为：$$|(x_1,y_1)-(x_2, y_2)| &#x3D; \sqrt{(x_1-x_2)^2+(y_1-y_2)^2}$$三维坐标系中$(x_1, y_1, z_1), (x_2, y_2, z_2)$欧式距离可表示为：$$|(x_1, y_1, z_1),(x_2, y_2, z_2)| &#x3D; \sqrt{(x_1-x_2)^2+(y_1-y_2)^2+(z_1-z_2)^2}$$以此类推，可以推广到N维空间.$$|(x ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2021/11/25/2021q4/103-2-ml-bays/" title="【机器学习】第三部分肆：朴素贝叶斯">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/AI-cover-black-white.webp" onerror="this.onerror=null;this.src='/img/404.png'" alt="【机器学习】第三部分肆：朴素贝叶斯"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/11/25/2021q4/103-2-ml-bays/" title="【机器学习】第三部分肆：朴素贝叶斯">【机器学习】第三部分肆：朴素贝叶斯</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-11-25T06:30:59.000Z" title="Created 2021-11-25 14:30:59">2021-11-25</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/">机器学习</a></span></div><div class="content">朴素贝叶斯是一组功能强大且易于训练的分类器，它使用贝叶斯定理来确定给定一组条件的结果的概率，“朴素”的含义是指所给定的条件都能独立存在和发生. 朴素贝叶斯是多用途分类器，能在很多不同的情景下找到它的应用，例如垃圾邮件过滤、自然语言处理等. 
概率定义概率是反映随机事件出现的可能性大小. 随机事件是指在相同条件下，可能出现也可能不出现的事件. 例如：
（1）抛一枚硬币，可能正面朝上，可能反面朝上，这是随机事件. 正&#x2F;反面朝上的可能性称为概率；
（2）掷骰子，掷出的点数为随机事件. 每个点数出现的可能性称为概率；
（3）一批商品包含良品、次品，随机抽取一件，抽得良品&#x2F;次品为随机事件. 经过大量反复试验，抽得次品率越来越接近于某个常数，则该常数为概率. 
我们可以将随机事件记为A或B，则P（A）, P（B）表示事件A或B的概率. 
联合概率与条件概率① 联合概率指包含多个条件且所有条件同时成立的概率，记作$P ( A , B )$ ，或$P(AB)$，或$P(A \bigcap B)$
② 条件概率已知事件B发生的条件下，另一个事件A发生的概率称为条件概率，记为：$P(A ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2021/11/25/2021q4/103-2-ml-dt/" title="【机器学习】第三部分贰：决策树分类">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/AI-cover-black-white.webp" onerror="this.onerror=null;this.src='/img/404.png'" alt="【机器学习】第三部分贰：决策树分类"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/11/25/2021q4/103-2-ml-dt/" title="【机器学习】第三部分贰：决策树分类">【机器学习】第三部分贰：决策树分类</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-11-25T06:30:58.000Z" title="Created 2021-11-25 14:30:58">2021-11-25</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/">机器学习</a></span></div><div class="content">什么是决策树决策树是一种常见的机器学习方法，其核心思想是相同（或相似）的输入产生相同（或相似）的输出，通过树状结构来进行决策，其目的是通过对样本不同属性的判断决策，将具有相同属性的样本划分到一个叶子节点下，从而实现分类或回归. 以下是几个生活中关于决策树的示例.
【示例1】

男生看女生与女生看男生的决策树模型
【示例2】


挑选西瓜的决策树模型
在上述示例模型中，通过对西瓜一系列特征（色泽、根蒂、敲声等）的判断，最终我们得出结论：这是否为一个好瓜. 决策过程中提出的每个判定问题都是对某个属性的“测试”，例如“色泽=？”，“根蒂=？”. 每个测试的结果可能得到最终结论，也可能需要进行下一步判断，其考虑问题的范围是在上次决策结果限定范围之内. 例如若在“色泽=青绿”之后再判断“根蒂=？”. 

决策树的结构一般来说，一棵决策树包含一个根节点、若干个内部节点和若干个叶子节点. 叶子节点对应最终的决策结果，其它每个节点则对应与一个属性的测试. 最终划分到同一个叶子节点上的样本，具有相同的决策属性，可以对这些样本的值求平均值来实现回归，对这些样本进行投票（选取样本数量最多的类别）实现分类.  ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2021/11/25/2021q4/103-2-ml-svm/" title="【机器学习】第三部分叁：支持向量机（SVM）">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/AI-cover-black-white.webp" onerror="this.onerror=null;this.src='/img/404.png'" alt="【机器学习】第三部分叁：支持向量机（SVM）"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/11/25/2021q4/103-2-ml-svm/" title="【机器学习】第三部分叁：支持向量机（SVM）">【机器学习】第三部分叁：支持向量机（SVM）</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-11-25T06:30:58.000Z" title="Created 2021-11-25 14:30:58">2021-11-25</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/">机器学习</a></span></div><div class="content">基本概念什么是支持向量机支持向量机（Support Vector Machines）是一种二分类模型，在机器学习、计算机视觉、数据挖掘中广泛应用，主要用于解决数据分类问题，它的目的是寻找一个超平面来对样本进行分割，分割的原则是间隔最大化（即数据集的边缘点到分界线的距离d最大，如下图），最终转化为一个凸二次规划问题来求解。通常SVM用于二元分类问题，对于多元分类可将其分解为多个二元分类问题，再进行分类。所谓“支持向量”，就是下图中虚线穿过的边缘点。支持向量机就对应着能将数据正确划分并且间隔最大的直线（下图中红色直线）。

最优分类边界什么才是最优分类边界？什么条件下的分类边界为最优边界呢？

如图中的A，B两个样本点，B点被预测为正类的确信度要大于A点，所以SVM的目标是寻找一个超平面，使得离超平面较近的异类点之间能有更大的间隔，即不必考虑所有样本点，只需让求得的超平面使得离它近的点间隔最大。超平面可以用如下线性方程来描述：$$w^T x + b &#x3D; 0$$其中，$x&#x3D;(x_1;x_2;…;x_n)$，$w&#x3D;(w_1;w_2;…;w_n)$，$b$为偏置项. ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2021/11/25/2021q4/103-2-ml-lo/" title="【机器学习】第三部分壹：逻辑回归">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/AI-cover-black-white.webp" onerror="this.onerror=null;this.src='/img/404.png'" alt="【机器学习】第三部分壹：逻辑回归"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/11/25/2021q4/103-2-ml-lo/" title="【机器学习】第三部分壹：逻辑回归">【机器学习】第三部分壹：逻辑回归</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-11-25T06:30:57.000Z" title="Created 2021-11-25 14:30:57">2021-11-25</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/">机器学习</a></span></div><div class="content">逻辑回归概述什么是逻辑回归逻辑回归（Logistic Regression） 虽然被称为回归，但其实际上是分类模型，常用于二分类。逻辑回归因其简单、可并行化、可解释强而受到广泛应用。二分类（也称为逻辑分类）是常见的分类方法，是将一批样本或数据划分到两个类别，例如一次考试，根据成绩可以分为及格、不及格两个类别，如下表所示：



姓名
成绩
分类



Jerry
86
1


Tom
98
1


Lily
58
0


……
……
……


这就是逻辑分类，将连续值映射到两个类别中。
逻辑函数逻辑回归是一种广义的线性回归，其原理是利用线性模型根据输入计算输出（线性模型输出值为连续），并在逻辑函数作用下，将连续值转换为两个离散值（0或1），其表达式如下：$$y &#x3D; h(w_1x_1 + w_2x_2 + w_3x_3 + … + w_nx_n + b)$$其中，括号中的部分为线性模型，计算结果在函数$h()$的作用下，做二值化转换，函数$h()$的定义为：$$h&#x3D; \frac{1}{1+e^{-t}}$$$$\quad t&#x3D;w^Tx+b$$
该函数称为Si ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2021/11/25/2021q4/103-1-ml-dt/" title="【机器学习】第二部分下：决策树回归">     <img class="post_bg" data-lazy-src="https://image.discover304.top/ai/AI-cover-black-white.webp" onerror="this.onerror=null;this.src='/img/404.png'" alt="【机器学习】第二部分下：决策树回归"></a></div><div class="recent-post-info"><a class="article-title" href="/2021/11/25/2021q4/103-1-ml-dt/" title="【机器学习】第二部分下：决策树回归">【机器学习】第二部分下：决策树回归</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2021-11-25T06:27:23.000Z" title="Created 2021-11-25 14:27:23">2021-11-25</time></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag article-meta__icon"></i><a class="article-meta__tags" href="/tags/%E5%AD%A6%E4%B9%A0/">学习</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E8%AE%B0%E5%BD%95/">记录</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/Python/">Python</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E7%AC%94%E8%AE%B0/">笔记</a><span class="article-meta__link">•</span><a class="article-meta__tags" href="/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/">机器学习</a></span></div><div class="content">决策树回归核心思想：相似的输入必会产生相似的输出。例如预测某人薪资：
年龄：1-青年，2-中年，3-老年学历：1-本科，2-硕士，3-博士经历：1-出道，2-一般，3-老手，4-骨灰性别：1-男性，2-女性



年龄
学历
经历
性别
&#x3D;&#x3D;&gt;
薪资



1
1
1
1
&#x3D;&#x3D;&gt;
6000（低）


2
1
3
1
&#x3D;&#x3D;&gt;
10000（中）


3
3
4
1
&#x3D;&#x3D;&gt;
50000（高）


…
…
…
…
&#x3D;&#x3D;&gt;
…


1
3
2
2
&#x3D;&#x3D;&gt;
?


 12345678样本数量非常庞大  100W个样本换一种数据结构，来提高检索效率树形结构回归 ：  均值分类 ： 投票(概率)



为了提高搜索效率，使用树形数据结构处理样本数据：$$\text{年龄}&#x3D;1\left{\begin{aligned}\text{学历}1 \\text{学历}2 \\text{学历}3 \\end{aligned}\right.\quad\ ...</div></div></div><nav id="pagination"><div class="pagination"><a class="extend prev" rel="prev" href="/page/6/"><i class="fas fa-chevron-left fa-fw"></i></a><a class="page-number" href="/">1</a><span class="space">&hellip;</span><a class="page-number" href="/page/6/">6</a><span class="page-number current">7</span><a class="page-number" href="/page/8/">8</a><a class="page-number" href="/page/9/">9</a><a class="extend next" rel="next" href="/page/8/"><i class="fas fa-chevron-right fa-fw"></i></a></div></nav></div><div class="aside_content" id="aside_content"><div class="card-widget card-info"><div class="card-content"><div class="card-info-avatar is-center"><img class="avatar-img" data-lazy-src="/img/head.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/><div class="author-info__name">✨YangSier✨</div><div class="author-info__description">Love Everything You Like.</div></div><div class="card-info-data"><div class="card-info-data-item is-center"><a href="/archives/"><div class="headline">Articles</div><div class="length-num">243</div></a></div><div class="card-info-data-item is-center"><a href="/tags/"><div class="headline">Tags</div><div class="length-num">88</div></a></div><div class="card-info-data-item is-center"><a href="/categories/"><div class="headline">Categories</div><div class="length-num">23</div></a></div></div><a class="button--animated" id="card-info-btn" target="_blank" rel="noopener" href="https://space.bilibili.com/98639326"><i class="fab fa-bilibili"></i><span>Bilibili Me</span></a><div class="card-info-social-icons is-center"><a class="social-icon" href="https://github.com/Discover304" target="_blank" title="Github"><i class="fab fa-github"></i></a><a class="social-icon" href="https://blog.csdn.net/Discover304" target="_blank" title="CSDN"><i class="fa-solid fa-c"></i></a><a class="social-icon" href="https://www.zhihu.com/people/discover-56-86-75" target="_blank" title="知乎"><i class="fa-brands fa-zhihu"></i></a><a class="social-icon" href="mailto:hobart.yang@qq.com" target="_blank" title="Email"><i class="fas fa-envelope"></i></a><a class="social-icon" href="https://jq.qq.com/?_wv=1027&amp;k=EaGddTQg" target="_blank" title="QQ"><i class="fa-brands fa-qq"></i></a></div></div></div><div class="card-widget card-announcement"><div class="card-content"><div class="item-headline"><i class="fas fa-bullhorn card-announcement-animation"></i><span>Announcement</span></div><div class="announcement_content">✨动态更新：<p style="text-align:center">享受精彩大学生活中。</p>✨聊天划水QQ群：<p style="text-align:center"><a target="_blank" rel="noopener" href="https://jq.qq.com/?_wv=1027&k=EaGddTQg"><strong>兔叽の魔术工房</strong></a><br>942-848-525</p>✨我们的口号是：<p style="text-align:center; 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